114 citations · 261 across the 8 of their papers we have counts for
8 papers
Evaluation of contextual embeddings on less-resourced languages
Matej Ulčar, Aleš Žagar, Carlos S. Armendariz +4
The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives. Most existing work focuses…
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
Carlos Santos Armendariz, Matthew Purver, Matej Ulčar +5
State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Stand…
Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift
Matej Martinc, Petra Kralj Novak, Senja Pollak
We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddin…
Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods
Fasih Haider, Senja Pollak, Pierre Albert +1
Research in automatic affect recognition has seldom addressed the issue of computational resource utilization. With the advent of ambient intelligence technology which employs a va…
Supervised and Unsupervised Neural Approaches to Text Readability
Matej Martinc, Senja Pollak, Marko Robnik-Šikonja
We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language model…
Language comparison via network topology
Blaž Škrlj, Senja Pollak
Modeling relations between languages can offer understanding of language characteristics and uncover similarities and differences between languages. Automated methods applied to la…
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
Blaž Škrlj, Andraž Repar, Senja Pollak
Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. W…
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
Blaž Škrlj, Matej Martinc, Jan Kralj +2
The use of background knowledge is largely unexploited in text classification tasks. This paper explores word taxonomies as means for constructing new semantic features, which may…